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Existing technical issues related to biomass gasification process efficiency and environmental standards are preventing the technology to become more economically viable. In order to tackle those issues a lot of attention has been given to biomass gasification process predictive modelling. These models should be robust enough to predict process parameters during variable operating conditions. This could be accomplished either by changes of model input variables or by changes in model structure. This paper analyses the potential of neural network based modelling to predict process parameters during plant operation with variable operating conditions. Dynamic neural network based model for gasification purposes will be developed and its performance will be analysed based on measured data derived from a fixed bed biomass gasification plant operated by Technical University Dresden (TU Dresden). Dynamic neural network can predict process temperature with an average error less than 10% and in those terms performs better than multiple linear regression models. Average prediction error of syngas quality is lower than 30%. Developed model is applicable for online analysis of biomass gasification process under variable operating conditions. The model is automatically modified when new operating conditions occur.

Hybridisation of district heating systems can contribute to more efficient heat generation through cogeneration power plants or through an increase in the share of renewable energy sources in total energy consumption while reducing negative aspects of particular energy source utilisation. In this work, the performance of a hybrid district energy system for a small town in Croatia has been analysed. A mathematical model for process analysis and optimisation algorithm for optimal system configuration have been developed and described. The main goal of the system optimisation is to reduce heat production costs. Several energy sources for heat production have been considered in 8 different simulation cases. Simulation results show that the heat production costs could be reduced with introduction of different energy systems into an existing district heating system. Renewable energy based district heating systems could contribute to heat production costs decrease in district heating systems up to 30% in comparison with highly efficient heat production technologies based on conventional fuels.